Assessment of data center configurations and serviceability requirements is an important aspect of determining what services are required, how to deliver them, and how to price service level agreements. Full-scale assessments, requiring a professional services engagement can be cost-prohibitive and time consuming. On the other hand, cursory (or a non-existent) assessments can fail to take into account factors that will affect an organization's ability to adequately and profitably service the customer.
One procedure that may be utilized to provide an assessment of data center configuration and serviceability is a discriminate analysis. A discriminate analysis is a standard statistical technique that determines best fits of data samples to particular factors of the data center. However, discriminate analysis can result in “mythical” or impossible measures.
For instance, an example that illustrates this concept comes from an actual case study in which the Swiss Army wanted to determine whether there were “pure types” of facial structures so they could design face masks that would its soldiers. An attempt to identify principal components in a set of facial measurements resulted in mythological factors that did not correspond to any real or fictitious faces in the real world. For example, one statistical grouping from the analysis resulted in faces with negative distances between two points on the head. Clearly, such a measurement is impossible to achieve on a human face.
Discriminate analysis, along with many other types of statistical techniques, result in groupings that may fit the data perfectly but otherwise are not realistic measurements. Furthermore, these techniques may be time-consuming and cost prohibitive to run. As a result, a mechanism which rapidly and cost-efficiently characterizes the serviceability characteristics of a data center to avoid the pitfalls discussed above would be beneficial.